Since the support set \(\mathcal {D}_{\rm train}\)  provided to the learner is limited,
it is crucial to employ proper learning biases in the model to combat
overfitting.
Inspired by the success of prototypical
networks [1]}, we design our model based on the
assumption that features of each object class form a single cluster in the
embedding space.
We propose to use directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally when each cluster is distributed on the unit hypersphere.
Formally, we assume features of each foreground class follow vMF distribution with mean direction \({\mathbf {\theta }}\)  and positive concentration parameter \(\kappa \)
\(p^+_{{\mathbf {\theta }}}(\mathbf {x}) = \frac{1}{Z} \exp \left(\kappa {\mathbf {\theta }}^\top \mathbf {x}\right) \text{ s.t. }  \Vert  {\mathbf {\theta }} \Vert   = 1~,\) 
